1. Field of the Invention
The present invention relates to the field of networks and distributed systems and, more particularly, to methods for generating models for determining casualty relationships between the occurrences, and the sources, of problems in the on observable events resulting from such occurrences.
2. Description of Related Art
As computer networks and other systems have become more complex, their reliability has become dependent upon the successful detection and management of problems in the system. Problems can include faults, performance degradation, intrusion attempts and other exceptional operational conditions requiring handling. Problems generate observable events, and these events can be monitored, detected, reported, analyzed and acted upon by humans or by programs. However, as systems have become more complex, the rate at which observable events occur has increased super-linearly, making problem management more difficult.
As an example, when the number of computer nodes in a network increases, the network complexity increases super-linearly with the number of nodes, with a concomitant increase in the fault rate. Compounding this problem of network complexity is fault propagation between both machines and network protocol layers; these propagated faults can generate additional events.
Automated management systems can help to cope with this increase in the number and complexity of events by (1) automating the collection and reporting of events, thereby reducing the load on human operators or programs; (2) using event correlation techniques to group distinct events, thereby compressing the event stream into a form more easily managed by human operators; (3) mapping groups of events to their underlying causes, thus reducing the time between faults and repairs; and (4) automatically correcting diagnosed problems, thereby minimizing operator intervention.
However, it is difficult and almost impossible to accurately model the underlying system, particularly as the networks increase in size and complexity. Moreover, for complex phenomena, a network model representation can quickly grow to unmanageable size because of the number of components that are contained in the network and, consequently, in the model.
Hence, a need exists in the industry for automated methods for generating accurate networks models.